Center for Computational Sciences, University of Tsukuba, Tsukuba, Japan.
S'UIMIN inc., Shibuya, Japan.
Sci Rep. 2024 Sep 19;14(1):21894. doi: 10.1038/s41598-024-72612-8.
In-home automated scoring systems are in high demand; however, the current systems are not widely adopted in clinical settings. Problems with electrode contact and restriction on measurable signals often result in unstable and inaccurate scoring for clinical use. To address these issues, we propose a method based on ensemble of small sleep stage scoring models with different input signal sets. By excluding models that employ problematic signals from the voting process, our method can mitigate the effects of electrode contact failure. Comparative experiments demonstrated that our method could reduce the impact of contact problems and improve scoring accuracy for epochs with problematic signals by 8.3 points, while also decreasing the deterioration in scoring accuracy from 7.9 to 0.3 points compared to typical methods. Additionally, we confirmed that assigning different input sets to small models did not diminish the advantages of the ensemble but instead increased its efficacy. The proposed model can improve overall scoring accuracy and minimize the effect of problematic signals simultaneously, making in-home sleep stage scoring systems more suitable for clinical practice.
家庭自动化评分系统需求旺盛；然而，当前的系统并未广泛应用于临床环境中。电极接触问题以及可测量信号的限制常常导致不稳定和不准确的评分，无法用于临床。为了解决这些问题，我们提出了一种基于具有不同输入信号集的小型睡眠阶段评分模型的集成方法。通过在投票过程中排除使用有问题信号的模型，我们的方法可以减轻电极接触失败的影响。对比实验表明，与典型方法相比，我们的方法可以将有问题信号的epoch 的评分准确性提高 8.3 分，同时将因接触问题导致的评分准确性下降从 7.9 分降至 0.3 分。此外，我们还证实，为小模型分配不同的输入集并不会削弱集成的优势，反而会提高其效果。该模型可以提高整体评分准确性，并同时最小化有问题信号的影响，从而使家庭睡眠阶段评分系统更适合临床实践。
